High-precision calculation of gas saturation in organic shale pores using an intelligent fusion algorithm and a multi-mineral model

 Shale gas reservoirs have been the subject of intensifying research in recent years. In particular, gas saturation has received considerable attention as a key parameter reflecting the gas-bearing properties of reservoirs. However, no mature model exists for calculating the saturation of shale gas reservoirs due to the difficulty in calculating the gas saturation. This paper proposes a new gas saturation prediction method that combines model-driven and data-driven approaches. A multi-mineral petrophysical model is applied to derive the apparent saturation model. Using the calculated apparent saturation, matrix parameters and porosity curve as inputs, an intelligent fusion algorithm composed of five regression algorithms is employed to predict the gas saturation. The gas saturation prediction results in the Yongchuan block, Sichuan Basin, reveal that the model proposed in this paper boasts good reliability and a greatly improved prediction accuracy. The proposed model can greatly assist in calculating the gas saturation of shale gas reservoirs.Cited as: Zhu, L., Zhang, C., Zhang, Z., Zhou, X. High-precision calculation of gas saturation in organic shale pores using an intelligent fusion algorithm and a multi-mineral model. Advances in Geo-Energy Research, 2020, 4(2): 135-151, doi: 10.26804/ager.2020.02.0

Interaction of water droplets residing on a solid surface with wall-bounded shear flows

Droplet depinning under wind-forcing arises in a wide range of engineering scenarios. Better understanding of this phenomenon not only can optimize the design of relevant engineering systems, but also can help preventing system failures caused by unwanted droplet accumulation. This thesis focuses on physical phenomena associated with the onset of droplet motion in wall-bounded shear flows formed by a flow over a flat plate and impinging jets. Specifically, (i) the critical droplet depinning conditions, and (ii) the influence of droplets on the surrounding flows are considered. Boundary layer flows and impinging jets were generated by the recirculating wind tunnel and a custom jet facility at the University of Waterloo. The freestream velocity and the jet centreline velocity were programmed to ramp up at three accelerations dU/dt =1.2,2.2, and 4.4 m/s^2. Comprehensive characterization of the background shear flows for droplet depinning tests were carried out using hot-wire anemometry and particle image velocimetry (PIV). The influence of droplets on ambient shear flows were investigated using scaled-up droplet models representative of the morphological shapes of a sessile droplet (sessile) and a deformed droplet on the verge of depinning (runback). At a Reynolds number representative of critical depinning condition, flow development over droplet models exhibits general similarities to that over other surface-mounted smooth obstacles. In laminar boundary layers, the presence of droplet models significantly modifies the near-wall velocity profiles and promotes laminar-to-turbulent boundary transition in a similar fashion as in bypass transition. Aerodynamic drag on droplet models submerged in a laminar boundary layer of thickness comparable to the model height was estimated based on flow field measurements using control volume analysis. The drag coefficients of the sessile and runback models are C_D≈0.36 and 0.35, respectively, approximately 10% lower than the drag coefficient of the hemisphere. Although the difference in drag coefficients is not significant for the two droplet models, the runback model demonstrated a reduction in drag force as compared to the sessile model, which is proportional to its reduction in the frontal area. For a given solid model, drag decreases significantly with elevated turbulence intensity in the incoming flow. Real water droplets of 75,90,105, and 120 μL were tested in the flat plate boundary layer and impinging jets at orientation angles of 30^∘, 45^∘, 60^∘, and 90^∘ on substrate of anodized aluminium. Droplets in flat plate boundary layer have a constant depinning threshold of We_(h,crit)=7.5±0.5. By contrast, droplets in impinging jets exhibit much lower thresholds in the range 2≤We_(h,crit)≤4. The effect of droplet volume and flow acceleration on depinning thresholds is small as compared to that of the flow orientation angle. A strong power-law relation is demonstrated between We_(h,crit) and volumetric shape factor Κ, and an empirical relation is established to predict the critical depinning velocity based on droplet volume, length, and height

Vibrotactile Feedback for Application on Mobile Touch Screen Devices: Effects with Age

This thesis has investigated vibrotactile interactions for touch screen devices related to age, the study developed distinguishable vibrotactile patterns for evaluation by younger and older people, in order to inform the design process for the development of a haptic language. The study of haptic perception validated that the optimal sensation to vibration for both age groups is in the range of 100-300 Hz, which guides the design of the future vibrotactile patterns development. As part of the human perception study carried out, it was found that two of the seven semantic differential pairs tested, ‘slow-fast’ and ‘light-heavy’, are suitable to describe the feelings of haptic feedback for younger people however there was no clear agreement for older people. It is recommended that the magnitude estimation techniques can be used for the future experimental design. Finally, this study shows that haptic language could be developed using vibration with the respect to the parameters of amplitude, frequency, and frequency ramping. The amplitude of vibration plays a key role in determining whether people can adequately sense the message, whereas the frequency can be used to imply meaning. The study found that a signal at 200 Hz could be understood to have a positive meaning for the vibrotactile interaction. Frequency ramping could be an essential parameter to design a negative vibrotactile interaction, compared to amplitude ramping that has no significant influence for perception. Most people would require a certain level of training to learn a haptic language because humans have no pre- conception of vibrations other than as an alert. It is suggested that a scenario should be provided to the subjects for the valuation

Progress of Autophagy Related Research in the Treatment of Ophthalmic Diseases

Autophagy is a process in which some organelles and proteins are wrapped by cells into specific membranes and then transported to lysosomes to degrade these membranes, ultimately degrading small molecules and energy. Autophagy can make cells have a certain tolerance to starvation, and remove damaged organelles and protein structure dislocation caused by cell aging, so as to balance the intracellular environment. Autophagy includes autophagy molecules, microactive autophagy and macrophage autophagy. The mechanism characteristics of autophagy itself have aroused the upsurge of relevant application research, and more and more diseases are related to it. This paper reviews the research progress of autophagy in novel clinical application of autophagy

A deep learning framework based on Koopman operator for data-driven modeling of vehicle dynamics

Autonomous vehicles and driving technologies have received notable attention in the past decades. In autonomous driving systems, \textcolor{black}{the} information of vehicle dynamics is required in most cases for designing of motion planning and control algorithms. However, it is nontrivial for identifying a global model of vehicle dynamics due to the existence of strong non-linearity and uncertainty. Many efforts have resorted to machine learning techniques for building data-driven models, but it may suffer from interpretability and result in a complex nonlinear representation. In this paper, we propose a deep learning framework relying on an interpretable Koopman operator to build a data-driven predictor of the vehicle dynamics. The main idea is to use the Koopman operator for representing the nonlinear dynamics in a linear lifted feature space. The approach results in a global model that integrates the dynamics in both longitudinal and lateral directions. As the core contribution, we propose a deep learning-based extended dynamic mode decomposition (Deep EDMD) algorithm to learn a finite approximation of the Koopman operator. Different from other machine learning-based approaches, deep neural networks play the role of learning feature representations for EDMD in the framework of the Koopman operator. Simulation results in a high-fidelity CarSim environment are reported, which show the capability of the Deep EDMD approach in multi-step prediction of vehicle dynamics at a wide operating range. Also, the proposed approach outperforms the EDMD method, the multi-layer perception (MLP) method, and the Extreme Learning Machines-based EDMD (ELM-EDMD) method in terms of modeling performance. Finally, we design a linear MPC with Deep EDMD (DE-MPC) for realizing reference tracking and test the controller in the CarSim environment.Comment: 12 pages, 10 figures, 1 table, and 2 algorithm

Fe-based metallic glasses and dyes in fenton-like processes: Understanding their intrinsic correlation

Fe-based metallic glasses have been demonstrated as effective heterogeneous catalysts in Fenton-like processes for dye degradation. Yet, currently corresponding studies have limitations due to the limited study object (dyes) and the correlation between metallic glasses and dye pollutants in Fenton-like processes is still not comprehensively studied. Accordingly, this work intensively investigated the thermal catalytic behavior correlations between two Fe-based metallic glasses (Fe78Si9B13 and Fe73.5Si13.5B9Cu1Nb3) and eight different dyes. Results indicated a lower activation energy in the more active metallic glass and a dependence of the activation energy of Fe-based metallic glasses in dye solutions. In addition, a high H2O2 concentration led to a declined catalytic efficiency but a photo-enhanced Fenton-like process overcame this limitation at high concentration of H2O2 due to the decrease of pH and enhancement of irradiation. Furthermore, the average mineralization rates of Fe78Si9B13 and Fe73.5Si13.5B9Cu1Nb3 have been measured to be 42.7% and 12.6%, respectively, and the correlation between decolorization and mineralization revealed that a faster decolorization in a Fenton-like process contributed to a higher mineralization rate. This work provides an intrinsic viewpoint of the correlation between Fe-based metallic glasses and dyes in Fenton-like processes and holds the promise to further promote the industrial value of metallic glasses